Mapping targets to single cells in plaques.
Accompanying ‘AE_TEMPLATE’
Sander W. van der Laan, PhD | s.w.vanderlaan@gmail.com
2023-06-14
General Setup
Create a new analysis directories.
- general directory
- for plots
- for output of summary results
- for baseline tables
- for genetic analyses
- for Cox regression resultsUpdate all/some/none? [a/s/n]: Update all/some/none? [a/s/n]: ERA-CVD ‘druggable-MI-targets’
For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:
conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Wednesday, June 14, 2023 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Wednesday, June 14, 2023 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.
Background
Here we map the PCSK9 to single-cells from the plaques.
Load data
First we will load the data:
- scRNAseq experimental data and rename the cell types.
- Athero-Express clinical data.
Here we load the latest dataset from our Athero-Express single-cell RNA experiment.
# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
#
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))
scRNAseqDataAn object of class Seurat
36147 features across 4948 samples within 2 assays
Active assay: RNA (20111 features, 0 variable features)
1 other assay present: SCT
2 dimensional reductions calculated: pca, umapThe naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exhibit similar properties, i.e. similar defining markers (e.g. KIT).
We will rename the cell types to human readable names.
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")
[1] CD3+ T Cells I CD3+ T Cells IV CD34+ Endothelial Cells I
[4] CD3+ T Cells V CD3+CD56+ NK Cells II CD3+ T Cells VI
[7] CD68+IL18+TLR4+TREM2+ Resident macrophages CD3+CD56+ NK Cells I ACTA2+ Smooth Muscle Cells
[10] CD3+ T Cells II FOXP3+ T Cells CD34+ Endothelial Cells II
[13] CD3+ T Cells III CD68+CD1C+ Dendritic Cells CD68+CASP1+IL1B+SELL+ Inflammatory macrophages
[16] CD79A+ Class-switched Memory B Cells CD68+ABCA1+OLR1+TREM2+ Foam Cells CD68+KIT+ Mast Cells
[19] CD68+CD4+ Monocytes CD79+ Plasma B Cells
20 Levels: CD3+ T Cells I CD3+ T Cells II CD3+ T Cells III CD3+ T Cells IV CD68+IL18+TLR4+TREM2+ Resident macrophages ACTA2+ Smooth Muscle Cells ... CD79+ Plasma B Cellscelltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes",
"CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
"CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
"CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
# T-cells
"CD3+ T Cells I" = "CD3+ TC I",
"CD3+ T Cells II" = "CD3+ TC II",
"CD3+ T Cells III" = "CD3+ TC III",
"CD3+ T Cells IV" = "CD3+ TC IV",
"CD3+ T Cells V" = "CD3+ TC V",
"CD3+ T Cells VI" = "CD3+ TC VI",
"FOXP3+ T Cells" = "FOXP3+ TC",
# Endothelial cells
"CD34+ Endothelial Cells I" = "CD34+ EC I",
"CD34+ Endothelial Cells II" = "CD34+ EC II",
# SMC
"ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC",
# NK Cells
"CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
"CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
# Mast
"CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
"CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem",
"CD79+ Plasma B Cells" = "CD79+ BCplasma")
scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData,
celltypes)UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
Clinical data
Loading the Athero-Express clinical data.
# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
# "hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
"MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
"Neutrophils_rankNorm", "MastCells_rankNorm",
"IPH.bin", "VesselDensity_rankNorm",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
"PCSK9_plasma", "PCSK9_plasma_rankNorm")
basetable_bin = c("Gender", "Artery_summary",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAESCRNA: baseline characteristics
Preparation
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)distinct: removed 4,902 rows (99%), 46 rows remainingscRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)[1] 46 1231# Replace missing data
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)
na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A",
"Not Available", "Not available",
"missing",
"-999", "-99",
"No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.
scRNAseqDataMetaAE %>%
replace_with_na_all(condition = ~.x %in% na_strings)cat("====================================================================================================")====================================================================================================SELECTION THE SHIZZLE- sanity checking PRIOR to selectionlibrary(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany") Hospital
Sex St. Antonius, Nieuwegein UMC Utrecht <NA>
female 0 19 0
male 0 26 0
<NA> 0 0 1ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany") Artery
Sex female male <NA>
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0
carotid (left & right) 19 25 0
femoral/iliac (left, right or both sides) 0 0 0
other carotid arteries (common, external) 0 1 0
carotid bypass and injury (left, right or both sides) 0 0 0
aneurysmata (carotid & femoral) 0 0 0
aorta 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0
<NA> 0 0 1 ae.gender
ae.ic female male <NA>
missing 0 0 0
no, died 0 0 0
yes 9 14 0
yes, health treatment when possible 5 7 0
yes, no health treatment 2 2 0
yes, no health treatment, no commercial business 1 2 0
yes, no tissue, no commerical business 0 0 0
yes, no tissue, no questionnaires, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no questionnaires, health treatment when possible 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commerical business 0 0 0
yes, no health treatment, no medical info, no commercial business 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business 0 0 0
yes, no questionnaires, no health treatment 0 0 0
yes, no tissue, no health treatment 0 0 0
yes, no tissue, no questionnaires 0 0 0
yes, no tissue, health treatment when possible 0 0 0
yes, no tissue 0 0 0
yes, no commerical business 1 1 0
yes, health treatment when possible, no commercial business 0 0 0
yes, no medical info, no commercial business 0 0 0
yes, no questionnaires 0 0 0
yes, no tissue, no questionnaires, no health treatment, no medical info 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commercial business 0 0 0
yes, no medical info 0 0 0
yes, no questionnaires, no commercial business 0 0 0
yes, no questionnaires, no health treatment, no medical info 0 0 0
yes, no questionnaires, health treatment when possible, no commercial business 0 0 0
yes, no health treatment, no medical info 0 0 0
no, doesn't want to 0 0 0
no, unable to sign 0 0 0
no, no reaction 0 0 0
no, lost 0 0 0
no, too old 0 0 0
yes, no medical info, health treatment when possible 1 0 0
no (never asked for IC because there was no tissue) 0 0 0
yes, no medical info, no commercial business, health treatment when possible 0 0 0
no, endpoint 0 0 0
wil niets invullen, wel alles gebruiken 0 0 0
second informed concents: yes, no commercial business 0 0 0
nooit geincludeerd 0 0 0
yes, not outside EU 0 0 0
yes, no DNA 0 0 0
<NA> 0 0 1rm(ae.gender, ae.hospital, ae.artery, ae.ic)
scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd" &
informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no commerical business" &
informedconsent != "yes, health treatment when possible, no commercial business" &
informedconsent != "yes, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "yes, no questionnaires, no commercial business" &
informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" &
informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)[1] 39 1231Baseline
Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.
===========================================================================================CREATE BASELINE TABLE# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = scRNAseqDataMetaAE.all, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]Warning: These variables only have NA/NaN: MAC_rankNorm SMC_rankNorm Neutrophils_rankNorm MastCells_rankNorm IPH.bin VesselDensity_rankNorm PCSK9_plasma PCSK9_plasma_rankNorm Dropped
level Overall
n 39
Hospital (%) St. Antonius, Nieuwegein 0.0
UMC Utrecht 100.0
ORyear (%) No data available/missing 0.0
2002 0.0
2003 0.0
2004 0.0
2005 0.0
2006 0.0
2007 0.0
2008 0.0
2009 0.0
2010 0.0
2011 0.0
2012 0.0
2013 0.0
2014 0.0
2015 0.0
2016 0.0
2017 0.0
2018 51.3
2019 35.9
2020 10.3
2021 2.6
2022 0.0
Artery_summary (%) No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0.0
carotid (left & right) 97.4
femoral/iliac (left, right or both sides) 0.0
other carotid arteries (common, external) 2.6
carotid bypass and injury (left, right or both sides) 0.0
aneurysmata (carotid & femoral) 0.0
aorta 0.0
other arteries (renal, popliteal, vertebral) 0.0
femoral bypass, angioseal and injury (left, right or both sides) 0.0
Age (mean (SD)) 72.077 (8.183)
Gender (%) female 41.0
male 59.0
TC_final (mean (SD)) 4.533 (1.252)
LDL_final (mean (SD)) 2.676 (1.013)
HDL_final (mean (SD)) 1.135 (0.229)
TG_final (mean (SD)) 1.927 (1.093)
systolic (mean (SD)) 150.842 (27.013)
diastoli (mean (SD)) 79.711 (16.432)
GFR_MDRD (mean (SD)) 79.036 (31.777)
BMI (mean (SD)) 26.332 (3.962)
KDOQI (%) No data available/missing 0.0
Normal kidney function 28.2
CKD 2 (Mild) 33.3
CKD 3 (Moderate) 28.2
CKD 4 (Severe) 0.0
CKD 5 (Failure) 0.0
<NA> 10.3
BMI_WHO (%) No data available/missing 0.0
Underweight 2.6
Normal 33.3
Overweight 38.5
Obese 17.9
<NA> 7.7
SmokerStatus (%) Current smoker 28.2
Ex-smoker 53.8
Never smoked 12.8
<NA> 5.1
AlcoholUse (%) No 38.5
Yes 53.8
<NA> 7.7
DiabetesStatus (%) Control (no Diabetes Dx/Med) 71.8
Diabetes 28.2
Hypertension.selfreport (%) No data available/missing 0.0
no 7.7
yes 87.2
<NA> 5.1
Hypertension.selfreportdrug (%) No data available/missing 0.0
no 7.7
yes 87.2
<NA> 5.1
Hypertension.composite (%) No data available/missing 0.0
no 7.7
yes 92.3
Hypertension.drugs (%) No data available/missing 0.0
no 10.3
yes 84.6
<NA> 5.1
Med.anticoagulants (%) No data available/missing 0.0
no 87.2
yes 5.1
<NA> 7.7
Med.all.antiplatelet (%) No data available/missing 0.0
no 20.5
yes 74.4
<NA> 5.1
Med.Statin.LLD (%) No data available/missing 0.0
no 20.5
yes 74.4
<NA> 5.1
Stroke_Dx (%) Missing 0.0
No stroke diagnosed 56.4
Stroke diagnosed 43.6
sympt (%) missing 0.0
Asymptomatic 15.4
TIA 17.9
minor stroke 25.6
Major stroke 10.3
Amaurosis fugax 15.4
Four vessel disease 0.0
Vertebrobasilary TIA 0.0
Retinal infarction 2.6
Symptomatic, but aspecific symtoms 2.6
Contralateral symptomatic occlusion 0.0
retinal infarction 2.6
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0
retinal infarction + TIAs 0.0
Ocular ischemic syndrome 7.7
ischemisch glaucoom 0.0
subclavian steal syndrome 0.0
TGA 0.0
Symptoms.5G (%) Asymptomatic 15.4
Ocular 23.1
Other 2.6
Retinal infarction 5.1
Stroke 35.9
TIA 17.9
AsymptSympt (%) Asymptomatic 15.4
Ocular and others 30.8
Symptomatic 53.8
AsymptSympt2G (%) Asymptomatic 15.4
Symptomatic 84.6
Symptoms.Update2G (%) Asymptomatic 15.4
Symptomatic 84.6
Symptoms.Update3G (%) Asymptomatic 15.4
Symptomatic 84.6
Unclear 0.0
indexsymptoms_latest_4g (mean (SD)) 1.769 (1.111)
restenos (%) missing 0.0
de novo 100.0
restenosis 0.0
stenose bij angioseal na PTCA 0.0
stenose (%) missing 0.0
0-49% 2.6
50-70% 10.3
70-90% 46.2
90-99% 25.6
100% (Occlusion) 0.0
NA 0.0
50-99% 0.0
70-99% 15.4
99 0.0
CAD_history (%) Missing 0.0
No history CAD 79.5
History CAD 20.5
PAOD (%) missing/no data 0.0
no 84.6
yes 15.4
Peripheral.interv (%) no 76.9
yes 23.1
EP_composite (%) No data available. 0.0
No composite endpoints 82.1
Composite endpoints 12.8
<NA> 5.1
EP_composite_time (mean (SD)) 522.597 (3078.209)
EP_major (%) No data available. 0.0
No major events (endpoints) 87.2
Major events (endpoints) 7.7
<NA> 5.1
EP_major_time (mean (SD)) 522.681 (3078.194)
Macrophages.bin (%) no/minor 2.6
moderate/heavy 0.0
<NA> 97.4
SMC.bin (%) no/minor 0.0
moderate/heavy 2.6
<NA> 97.4
Calc.bin (%) no/minor 2.6
moderate/heavy 0.0
<NA> 97.4
Collagen.bin (%) no/minor 0.0
moderate/heavy 2.6
<NA> 97.4
Fat.bin_10 (%) <10% 0.0
>10% 2.6
<NA> 97.4
Fat.bin_40 (%) <40% 2.6
>40% 0.0
<NA> 97.4
OverallPlaquePhenotype (%) atheromatous 0.0
fibroatheromatous 2.6
fibrous 0.0
<NA> 97.4
Plaque_Vulnerability_Index (%) 0 97.4
1 2.6
2 0.0
3 0.0
4 0.0
5 0.0 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.32pts.after_qc.IC_commercial.BaselineTable.xlsx"),
# format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
# rowNames = TRUE, colNames = TRUE,
# sheetName = "AESCRNA", overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"),
format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
rowNames = TRUE, colNames = TRUE,
sheetName = "AESCRNA_CEA", overwrite = TRUE)AESCRNA
Quality control
Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.
How many cells per type ...?integer(0)# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))
# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
cat("\n\nHow many cells per patient ...?")
How many cells per patient ...?
4530 4675 4440 4605 4653 4472 4458 4455 4476 4587 4496 4601 4502 4501 4571 4478 4448 4477 4452 4459 4520 4602 4489 4432 4495
3 4 6 7 20 22 35 54 59 60 70 70 73 75 76 77 80 84 92 94 96 96 97 99 102
4545 4558 4480 4447 4500 4513 4535 4676 4486 4470 4487 4546 4488 4521 4580 4491 4541 4450 4542 4453 4443
106 107 112 114 116 123 130 135 137 144 144 144 146 161 163 175 178 205 213 222 422
Visualizing these ratio's per study number and sample ...?UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())Saving 7.29 x 4.51 in imageSaving 7.29 x 4.51 in image
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)),
# cex.axis = 1.0, cex.names = 0.5, las = 1,
# col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)),
# cex.axis = 1.0, cex.names = 0.5, las = 2,
# col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()Visualisations
Let’s project known cellular markers.
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols = c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols = c("#ECECEC", "#DB003F"))
# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols = c("#ECECEC", "#DB003F"))
Targets of interest:
We check whether the targets genes were sequenced using our method. In case some genes are not available in our data we could filter them here.
[1] "CXCL10" "PCSK9" "COL4A1" "COL4A2" "COL3A" "COL2A" "LDLR" "CD36" This code is just an example to filter the list from genes that are not in the data.
- COL3A ==> not found
- COL2A ==> not found
gene_list_rm <- c("COL3A", "COL2A")
temp = target_genes[!target_genes %in% gene_list_rm]
target_genes_qc <- c(temp)
# gene_list_qc <- gene_list
#
# for debug
# gene_list_qc_replace <- c("MRTFA")
# target_genes_qc <- target_genes
target_genes_qc[1] "CXCL10" "PCSK9" "COL4A1" "COL4A2" "LDLR" "CD36" Expression in cell communities
library(RColorBrewer)
p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
cols = "RdBu")
p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())
rm(p1)
# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
# cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
# combine = TRUE)
#
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())# VlnPlot(scRNAseqData, features = "DUSP27")
# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")),
dir.create(file.path(PLOT_loc, "/VlnPlot")),
FALSE)[1] FALSEVlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")
for (GENE in target_genes_qc){
print(paste0("Projecting the expression of ", GENE, "."))
vp1 <- VlnPlot(scRNAseqData, features = GENE) +
xlab("cell communities") +
ylab(bquote("normalized expression")) +
theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
axis.title.y = element_text(color = "#000000", size = 14, face = "bold"),
legend.position = "none")
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
# print(vp1)
}[1] "Projecting the expression of CXCL10."Saving 7 x 7 in image[1] "Projecting the expression of PCSK9."
[1] "Projecting the expression of COL4A1."
[1] "Projecting the expression of COL4A2."
[1] "Projecting the expression of LDLR."
[1] "Projecting the expression of CD36."Differential expression between cell communities
Here we project genes to only the broad cell communities:
- macrophages
- endothelial cells
- smooth muscle cells
- T-cells
- B-cells
- Mast cells
- NK-cells
- Mixed cells
Macrophages
[1] CD3+ TC I CD3+ TC IV CD34+ EC I CD3+ TC V
[5] CD3+CD56+ NK II CD3+ TC VI CD68+IL18+TLR4+TREM2+ MRes CD3+CD56+ NK I
[9] ACTA2+ SMC CD3+ TC II FOXP3+ TC CD34+ EC II
[13] CD3+ TC III CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf CD79A+ BCmem
[17] CD68+ABCA1+OLR1+TREM2+ FC CD68+KIT+ MC CD68+CD4+ Mono CD79+ BCplasma
20 Levels: CD68+CD4+ Mono CD68+IL18+TLR4+TREM2+ MRes CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf ... CD79+ BCplasmaComparison between the macrophages cell communities (CD14/CD68+), and all other communities.
MAC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC"),
ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf",
#"CD68+CD1C+ DC",
#"CD68+CD4+ Mono",
#"CD68+IL18+TLR4+TREM2+ MRes",
#"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~58s
|++ | 2 % ~56s
|++ | 3 % ~55s
|+++ | 4 % ~54s
|+++ | 5 % ~53s
|++++ | 6 % ~53s
|++++ | 7 % ~52s
|+++++ | 8 % ~51s
|+++++ | 9 % ~51s
|++++++ | 10% ~50s
|++++++ | 11% ~50s
|+++++++ | 12% ~49s
|+++++++ | 13% ~49s
|++++++++ | 14% ~48s
|++++++++ | 15% ~48s
|+++++++++ | 16% ~47s
|+++++++++ | 17% ~47s
|++++++++++ | 18% ~46s
|++++++++++ | 19% ~46s
|+++++++++++ | 20% ~45s
|+++++++++++ | 21% ~45s
|++++++++++++ | 22% ~44s
|++++++++++++ | 23% ~43s
|+++++++++++++ | 24% ~43s
|+++++++++++++ | 25% ~42s
|++++++++++++++ | 26% ~42s
|++++++++++++++ | 27% ~41s
|+++++++++++++++ | 28% ~40s
|+++++++++++++++ | 29% ~40s
|++++++++++++++++ | 30% ~39s
|++++++++++++++++ | 31% ~39s
|+++++++++++++++++ | 32% ~38s
|+++++++++++++++++ | 33% ~38s
|++++++++++++++++++ | 34% ~38s
|++++++++++++++++++ | 35% ~37s
|+++++++++++++++++++ | 36% ~37s
|+++++++++++++++++++ | 37% ~36s
|++++++++++++++++++++ | 38% ~36s
|++++++++++++++++++++ | 39% ~35s
|+++++++++++++++++++++ | 40% ~35s
|+++++++++++++++++++++ | 41% ~34s
|++++++++++++++++++++++ | 42% ~33s
|++++++++++++++++++++++ | 43% ~33s
|+++++++++++++++++++++++ | 44% ~32s
|+++++++++++++++++++++++ | 45% ~32s
|++++++++++++++++++++++++ | 46% ~32s
|++++++++++++++++++++++++ | 47% ~31s
|+++++++++++++++++++++++++ | 48% ~31s
|+++++++++++++++++++++++++ | 49% ~30s
|++++++++++++++++++++++++++ | 51% ~30s
|++++++++++++++++++++++++++ | 52% ~29s
|+++++++++++++++++++++++++++ | 53% ~28s
|+++++++++++++++++++++++++++ | 54% ~28s
|++++++++++++++++++++++++++++ | 55% ~27s
|++++++++++++++++++++++++++++ | 56% ~26s
|+++++++++++++++++++++++++++++ | 57% ~26s
|+++++++++++++++++++++++++++++ | 58% ~25s
|++++++++++++++++++++++++++++++ | 59% ~24s
|++++++++++++++++++++++++++++++ | 60% ~24s
|+++++++++++++++++++++++++++++++ | 61% ~23s
|+++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~22s
|++++++++++++++++++++++++++++++++ | 64% ~21s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|+++++++++++++++++++++++++++++++++ | 66% ~20s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|++++++++++++++++++++++++++++++++++ | 68% ~19s
|+++++++++++++++++++++++++++++++++++ | 69% ~18s
|+++++++++++++++++++++++++++++++++++ | 70% ~18s
|++++++++++++++++++++++++++++++++++++ | 71% ~17s
|++++++++++++++++++++++++++++++++++++ | 72% ~17s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|+++++++++++++++++++++++++++++++++++++ | 74% ~15s
|++++++++++++++++++++++++++++++++++++++ | 75% ~15s
|++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=57s MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
lab = rownames(MAC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Macrophage markers\n(Macrophage communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"),
plot = MAC_Volcano_TargetsA)
The target results are given below and written to a file.
Smooth muscle cells
Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.
SMC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("ACTA2+ SMC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
#"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 19s
|++ | 2 % ~01m 14s
|++ | 3 % ~01m 12s
|+++ | 4 % ~01m 11s
|+++ | 5 % ~01m 09s
|++++ | 6 % ~01m 09s
|++++ | 7 % ~01m 08s
|+++++ | 8 % ~01m 07s
|+++++ | 9 % ~01m 06s
|++++++ | 10% ~01m 05s
|++++++ | 11% ~01m 04s
|+++++++ | 12% ~01m 04s
|+++++++ | 13% ~01m 03s
|++++++++ | 14% ~01m 02s
|++++++++ | 15% ~01m 01s
|+++++++++ | 16% ~01m 00s
|+++++++++ | 17% ~60s
|++++++++++ | 18% ~59s
|++++++++++ | 19% ~58s
|+++++++++++ | 20% ~57s
|+++++++++++ | 21% ~56s
|++++++++++++ | 22% ~56s
|++++++++++++ | 23% ~55s
|+++++++++++++ | 24% ~54s
|+++++++++++++ | 26% ~53s
|++++++++++++++ | 27% ~53s
|++++++++++++++ | 28% ~52s
|+++++++++++++++ | 29% ~51s
|+++++++++++++++ | 30% ~50s
|++++++++++++++++ | 31% ~50s
|++++++++++++++++ | 32% ~49s
|+++++++++++++++++ | 33% ~48s
|+++++++++++++++++ | 34% ~48s
|++++++++++++++++++ | 35% ~47s
|++++++++++++++++++ | 36% ~46s
|+++++++++++++++++++ | 37% ~45s
|+++++++++++++++++++ | 38% ~44s
|++++++++++++++++++++ | 39% ~44s
|++++++++++++++++++++ | 40% ~43s
|+++++++++++++++++++++ | 41% ~42s
|+++++++++++++++++++++ | 42% ~42s
|++++++++++++++++++++++ | 43% ~41s
|++++++++++++++++++++++ | 44% ~40s
|+++++++++++++++++++++++ | 45% ~39s
|+++++++++++++++++++++++ | 46% ~39s
|++++++++++++++++++++++++ | 47% ~39s
|++++++++++++++++++++++++ | 48% ~39s
|+++++++++++++++++++++++++ | 49% ~38s
|+++++++++++++++++++++++++ | 50% ~37s
|++++++++++++++++++++++++++ | 51% ~37s
|+++++++++++++++++++++++++++ | 52% ~36s
|+++++++++++++++++++++++++++ | 53% ~35s
|++++++++++++++++++++++++++++ | 54% ~34s
|++++++++++++++++++++++++++++ | 55% ~33s
|+++++++++++++++++++++++++++++ | 56% ~01m 04s
|+++++++++++++++++++++++++++++ | 57% ~01m 02s
|++++++++++++++++++++++++++++++ | 58% ~60s
|++++++++++++++++++++++++++++++ | 59% ~58s
|+++++++++++++++++++++++++++++++ | 60% ~56s
|+++++++++++++++++++++++++++++++ | 61% ~54s
|++++++++++++++++++++++++++++++++ | 62% ~52s
|++++++++++++++++++++++++++++++++ | 63% ~50s
|+++++++++++++++++++++++++++++++++ | 64% ~49s
|+++++++++++++++++++++++++++++++++ | 65% ~47s
|++++++++++++++++++++++++++++++++++ | 66% ~45s
|++++++++++++++++++++++++++++++++++ | 67% ~43s
|+++++++++++++++++++++++++++++++++++ | 68% ~42s
|+++++++++++++++++++++++++++++++++++ | 69% ~40s
|++++++++++++++++++++++++++++++++++++ | 70% ~39s
|++++++++++++++++++++++++++++++++++++ | 71% ~37s
|+++++++++++++++++++++++++++++++++++++ | 72% ~35s
|+++++++++++++++++++++++++++++++++++++ | 73% ~34s
|++++++++++++++++++++++++++++++++++++++ | 74% ~32s
|++++++++++++++++++++++++++++++++++++++ | 76% ~31s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~30s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~28s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~25s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 52sSMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
lab = rownames(SMC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "SMC markers\n(SMC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"),
plot = SMC_Volcano_TargetsA)
The target results are given below and written to a file.
Endothelial cells
Comparison between the endothelial cell communities (CD34+), and all other communities.
EC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD34+ EC I",
"CD34+ EC II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
# "CD34+ EC I",
# "CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 03s
|++ | 2 % ~01m 01s
|++ | 3 % ~60s
|+++ | 4 % ~59s
|+++ | 5 % ~59s
|++++ | 6 % ~58s
|++++ | 7 % ~58s
|+++++ | 8 % ~57s
|+++++ | 9 % ~56s
|++++++ | 10% ~56s
|++++++ | 11% ~55s
|+++++++ | 12% ~54s
|+++++++ | 13% ~53s
|++++++++ | 14% ~53s
|++++++++ | 15% ~53s
|+++++++++ | 16% ~52s
|+++++++++ | 17% ~52s
|++++++++++ | 18% ~51s
|++++++++++ | 19% ~50s
|+++++++++++ | 20% ~49s
|+++++++++++ | 21% ~49s
|++++++++++++ | 22% ~48s
|++++++++++++ | 23% ~48s
|+++++++++++++ | 24% ~47s
|+++++++++++++ | 25% ~01m 48s
|++++++++++++++ | 26% ~01m 44s
|++++++++++++++ | 27% ~01m 41s
|+++++++++++++++ | 28% ~01m 37s
|+++++++++++++++ | 29% ~01m 34s
|++++++++++++++++ | 30% ~01m 31s
|++++++++++++++++ | 31% ~01m 28s
|+++++++++++++++++ | 32% ~01m 26s
|+++++++++++++++++ | 33% ~01m 23s
|++++++++++++++++++ | 34% ~01m 22s
|++++++++++++++++++ | 35% ~01m 19s
|+++++++++++++++++++ | 36% ~01m 17s
|+++++++++++++++++++ | 37% ~01m 15s
|++++++++++++++++++++ | 38% ~01m 13s
|++++++++++++++++++++ | 39% ~01m 10s
|+++++++++++++++++++++ | 40% ~01m 08s
|+++++++++++++++++++++ | 41% ~01m 06s
|++++++++++++++++++++++ | 42% ~01m 05s
|++++++++++++++++++++++ | 43% ~01m 03s
|+++++++++++++++++++++++ | 44% ~01m 01s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~56s
|+++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|++++++++++++++++++++++++++ | 51% ~51s
|++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~48s
|+++++++++++++++++++++++++++ | 54% ~47s
|++++++++++++++++++++++++++++ | 55% ~46s
|++++++++++++++++++++++++++++ | 56% ~44s
|+++++++++++++++++++++++++++++ | 57% ~43s
|+++++++++++++++++++++++++++++ | 58% ~42s
|++++++++++++++++++++++++++++++ | 59% ~40s
|++++++++++++++++++++++++++++++ | 60% ~39s
|+++++++++++++++++++++++++++++++ | 61% ~38s
|+++++++++++++++++++++++++++++++ | 62% ~37s
|++++++++++++++++++++++++++++++++ | 63% ~36s
|++++++++++++++++++++++++++++++++ | 64% ~34s
|+++++++++++++++++++++++++++++++++ | 65% ~33s
|+++++++++++++++++++++++++++++++++ | 66% ~32s
|++++++++++++++++++++++++++++++++++ | 67% ~31s
|++++++++++++++++++++++++++++++++++ | 68% ~30s
|+++++++++++++++++++++++++++++++++++ | 69% ~29s
|+++++++++++++++++++++++++++++++++++ | 70% ~28s
|++++++++++++++++++++++++++++++++++++ | 71% ~27s
|++++++++++++++++++++++++++++++++++++ | 72% ~26s
|+++++++++++++++++++++++++++++++++++++ | 73% ~25s
|+++++++++++++++++++++++++++++++++++++ | 74% ~24s
|++++++++++++++++++++++++++++++++++++++ | 75% ~23s
|++++++++++++++++++++++++++++++++++++++ | 76% ~22s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~20s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 21sEC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
lab = rownames(EC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Endothelial cell markers\n(EC communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"),
plot = EC_Volcano_TargetsA)
The target results are given below and written to a file.
T-cells
Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.
TC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
# "CD3+ TC I",
# "CD3+ TC II",
# "CD3+ TC III",
# "CD3+ TC IV",
# "CD3+ TC V",
# "CD3+ TC VI",
# "FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~01m 00s
|++ | 2 % ~54s
|++ | 3 % ~52s
|+++ | 4 % ~50s
|+++ | 5 % ~49s
|++++ | 6 % ~48s
|++++ | 7 % ~48s
|+++++ | 8 % ~02h 58m 55s
|+++++ | 9 % ~02h 37m 22s
|++++++ | 10% ~02h 20m 06s
|++++++ | 11% ~02h 05m 59s
|+++++++ | 12% ~01h 54m 13s
|+++++++ | 13% ~01h 44m 16s
|++++++++ | 14% ~01h 35m 44s
|++++++++ | 15% ~01h 28m 20s
|+++++++++ | 16% ~01h 21m 51s
|+++++++++ | 17% ~01h 16m 08s
|++++++++++ | 18% ~01h 11m 03s
|++++++++++ | 19% ~01h 06m 30s
|+++++++++++ | 20% ~01h 02m 25s
|+++++++++++ | 21% ~58m 42s
|++++++++++++ | 22% ~55m 20s
|++++++++++++ | 23% ~52m 16s
|+++++++++++++ | 24% ~49m 27s
|+++++++++++++ | 26% ~46m 51s
|++++++++++++++ | 27% ~44m 27s
|++++++++++++++ | 28% ~01h 21m 53s
|+++++++++++++++ | 29% ~01h 17m 52s
|+++++++++++++++ | 30% ~01h 14m 08s
|++++++++++++++++ | 31% ~01h 10m 38s
|++++++++++++++++ | 32% ~01h 07m 22s
|+++++++++++++++++ | 33% ~01h 04m 19s
|+++++++++++++++++ | 34% ~01h 01m 26s
|++++++++++++++++++ | 35% ~58m 44s
|++++++++++++++++++ | 36% ~56m 10s
|+++++++++++++++++++ | 37% ~53m 45s
|+++++++++++++++++++ | 38% ~51m 28s
|++++++++++++++++++++ | 39% ~49m 19s
|++++++++++++++++++++ | 40% ~47m 15s
|+++++++++++++++++++++ | 41% ~45m 18s
|+++++++++++++++++++++ | 42% ~43m 27s
|++++++++++++++++++++++ | 43% ~41m 41s
|++++++++++++++++++++++ | 44% ~39m 60s
|+++++++++++++++++++++++ | 45% ~38m 23s
|+++++++++++++++++++++++ | 46% ~36m 51s
|++++++++++++++++++++++++ | 47% ~35m 22s
|++++++++++++++++++++++++ | 48% ~33m 58s
|+++++++++++++++++++++++++ | 49% ~32m 37s
|+++++++++++++++++++++++++ | 50% ~31m 19s
|++++++++++++++++++++++++++ | 51% ~30m 04s
|+++++++++++++++++++++++++++ | 52% ~28m 53s
|+++++++++++++++++++++++++++ | 53% ~27m 44s
|++++++++++++++++++++++++++++ | 54% ~26m 37s
|++++++++++++++++++++++++++++ | 55% ~25m 33s
|+++++++++++++++++++++++++++++ | 56% ~24m 31s
|+++++++++++++++++++++++++++++ | 57% ~23m 32s
|++++++++++++++++++++++++++++++ | 58% ~22m 34s
|++++++++++++++++++++++++++++++ | 59% ~21m 39s
|+++++++++++++++++++++++++++++++ | 60% ~20m 45s
|+++++++++++++++++++++++++++++++ | 61% ~19m 53s
|++++++++++++++++++++++++++++++++ | 62% ~19m 03s
|++++++++++++++++++++++++++++++++ | 63% ~18m 15s
|+++++++++++++++++++++++++++++++++ | 64% ~17m 28s
|+++++++++++++++++++++++++++++++++ | 65% ~16m 42s
|++++++++++++++++++++++++++++++++++ | 66% ~15m 58s
|++++++++++++++++++++++++++++++++++ | 67% ~15m 15s
|+++++++++++++++++++++++++++++++++++ | 68% ~14m 34s
|+++++++++++++++++++++++++++++++++++ | 69% ~13m 53s
|++++++++++++++++++++++++++++++++++++ | 70% ~13m 14s
|++++++++++++++++++++++++++++++++++++ | 71% ~12m 36s
|+++++++++++++++++++++++++++++++++++++ | 72% ~11m 59s
|+++++++++++++++++++++++++++++++++++++ | 73% ~11m 23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~10m 48s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10m 13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09m 40s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09m 08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08m 36s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08m 05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07m 35s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~07m 06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06m 38s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~06m 10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05m 43s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05m 16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04m 50s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04m 25s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04m 00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03m 36s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03m 12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02m 49s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02m 26s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02m 04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01m 42s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01m 21s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01m 00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~40s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31m 43sTC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
lab = rownames(TC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "T-cell markers\n(T-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(TC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"),
plot = TC_Volcano_TargetsA)
The target results are given below and written to a file.
B-cells
Comparison between the B-cell communities (CD79A+), and all other communities.
BC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD79+ BCplasma",
"CD79A+ BCmem"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
"CD68+KIT+ MC"
# "CD79+ BCplasma",
# "CD79A+ BCmem"
))
| | 0 % ~calculating
|+ | 1 % ~43s
|+ | 2 % ~42s
|++ | 3 % ~41s
|++ | 4 % ~41s
|+++ | 5 % ~40s
|+++ | 6 % ~40s
|++++ | 7 % ~40s
|++++ | 8 % ~39s
|+++++ | 9 % ~39s
|+++++ | 10% ~38s
|++++++ | 11% ~38s
|++++++ | 12% ~37s
|+++++++ | 13% ~37s
|+++++++ | 14% ~37s
|++++++++ | 15% ~36s
|++++++++ | 16% ~36s
|+++++++++ | 17% ~35s
|+++++++++ | 18% ~35s
|++++++++++ | 19% ~35s
|++++++++++ | 20% ~34s
|+++++++++++ | 21% ~34s
|+++++++++++ | 22% ~33s
|++++++++++++ | 23% ~33s
|++++++++++++ | 24% ~32s
|+++++++++++++ | 25% ~32s
|+++++++++++++ | 26% ~31s
|++++++++++++++ | 27% ~31s
|++++++++++++++ | 28% ~31s
|+++++++++++++++ | 29% ~30s
|+++++++++++++++ | 30% ~30s
|++++++++++++++++ | 31% ~31s
|++++++++++++++++ | 32% ~30s
|+++++++++++++++++ | 33% ~30s
|+++++++++++++++++ | 34% ~29s
|++++++++++++++++++ | 35% ~29s
|++++++++++++++++++ | 36% ~28s
|+++++++++++++++++++ | 37% ~28s
|+++++++++++++++++++ | 38% ~27s
|++++++++++++++++++++ | 39% ~27s
|++++++++++++++++++++ | 40% ~26s
|+++++++++++++++++++++ | 41% ~26s
|+++++++++++++++++++++ | 42% ~26s
|++++++++++++++++++++++ | 43% ~25s
|++++++++++++++++++++++ | 44% ~25s
|+++++++++++++++++++++++ | 45% ~24s
|+++++++++++++++++++++++ | 46% ~24s
|++++++++++++++++++++++++ | 47% ~23s
|++++++++++++++++++++++++ | 48% ~23s
|+++++++++++++++++++++++++ | 49% ~22s
|+++++++++++++++++++++++++ | 50% ~22s
|++++++++++++++++++++++++++ | 51% ~21s
|++++++++++++++++++++++++++ | 52% ~21s
|+++++++++++++++++++++++++++ | 53% ~21s
|+++++++++++++++++++++++++++ | 54% ~20s
|++++++++++++++++++++++++++++ | 55% ~20s
|++++++++++++++++++++++++++++ | 56% ~19s
|+++++++++++++++++++++++++++++ | 57% ~19s
|+++++++++++++++++++++++++++++ | 58% ~18s
|++++++++++++++++++++++++++++++ | 59% ~18s
|++++++++++++++++++++++++++++++ | 60% ~17s
|+++++++++++++++++++++++++++++++ | 61% ~17s
|+++++++++++++++++++++++++++++++ | 62% ~16s
|++++++++++++++++++++++++++++++++ | 63% ~16s
|++++++++++++++++++++++++++++++++ | 64% ~16s
|+++++++++++++++++++++++++++++++++ | 65% ~15s
|+++++++++++++++++++++++++++++++++ | 66% ~15s
|++++++++++++++++++++++++++++++++++ | 67% ~14s
|++++++++++++++++++++++++++++++++++ | 68% ~14s
|+++++++++++++++++++++++++++++++++++ | 69% ~13s
|+++++++++++++++++++++++++++++++++++ | 70% ~13s
|++++++++++++++++++++++++++++++++++++ | 71% ~12s
|++++++++++++++++++++++++++++++++++++ | 72% ~12s
|+++++++++++++++++++++++++++++++++++++ | 73% ~12s
|+++++++++++++++++++++++++++++++++++++ | 74% ~11s
|++++++++++++++++++++++++++++++++++++++ | 75% ~11s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~09s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~08s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~07s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~21s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17m 05sBC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
lab = rownames(BC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "B-cell markers\n(B-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(BC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"),
plot = BC_Volcano_TargetsA)
The target results are given below and written to a file.
Mast cells
Comparison between the mast cell communities (KIT+), and all other communities.
MC.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD68+KIT+ MC"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
"CD3+CD56+ NK I",
"CD3+CD56+ NK II",
# "CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~60s
|+ | 2 % ~56s
|++ | 3 % ~55s
|++ | 4 % ~55s
|+++ | 5 % ~55s
|+++ | 6 % ~55s
|++++ | 7 % ~54s
|++++ | 8 % ~54s
|+++++ | 9 % ~53s
|+++++ | 10% ~52s
|++++++ | 11% ~52s
|++++++ | 12% ~51s
|+++++++ | 13% ~51s
|+++++++ | 14% ~50s
|++++++++ | 15% ~49s
|++++++++ | 16% ~49s
|+++++++++ | 17% ~48s
|+++++++++ | 18% ~47s
|++++++++++ | 19% ~47s
|++++++++++ | 20% ~46s
|+++++++++++ | 21% ~45s
|+++++++++++ | 22% ~45s
|++++++++++++ | 23% ~44s
|++++++++++++ | 24% ~44s
|+++++++++++++ | 25% ~43s
|+++++++++++++ | 26% ~42s
|++++++++++++++ | 27% ~42s
|++++++++++++++ | 28% ~41s
|+++++++++++++++ | 29% ~42s
|+++++++++++++++ | 30% ~41s
|++++++++++++++++ | 31% ~41s
|++++++++++++++++ | 32% ~40s
|+++++++++++++++++ | 33% ~40s
|+++++++++++++++++ | 34% ~39s
|++++++++++++++++++ | 35% ~38s
|++++++++++++++++++ | 36% ~38s
|+++++++++++++++++++ | 37% ~37s
|+++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 39% ~36s
|++++++++++++++++++++ | 40% ~35s
|+++++++++++++++++++++ | 41% ~35s
|+++++++++++++++++++++ | 42% ~34s
|++++++++++++++++++++++ | 43% ~33s
|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
|+++++++++++++++++++++++ | 46% ~31s
|++++++++++++++++++++++++ | 47% ~31s
|++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~17m 02s
|+++++++++++++++++++++++++ | 50% ~16m 23s
|++++++++++++++++++++++++++ | 51% ~15m 45s
|++++++++++++++++++++++++++ | 52% ~15m 08s
|+++++++++++++++++++++++++++ | 53% ~14m 33s
|+++++++++++++++++++++++++++ | 54% ~13m 59s
|++++++++++++++++++++++++++++ | 55% ~13m 26s
|++++++++++++++++++++++++++++ | 56% ~12m 55s
|+++++++++++++++++++++++++++++ | 57% ~12m 24s
|+++++++++++++++++++++++++++++ | 58% ~24m 50s
|++++++++++++++++++++++++++++++ | 59% ~23m 50s
|++++++++++++++++++++++++++++++ | 60% ~22m 52s
|+++++++++++++++++++++++++++++++ | 61% ~21m 56s
|+++++++++++++++++++++++++++++++ | 62% ~21m 02s
|++++++++++++++++++++++++++++++++ | 63% ~20m 10s
|++++++++++++++++++++++++++++++++ | 64% ~19m 19s
|+++++++++++++++++++++++++++++++++ | 65% ~18m 30s
|+++++++++++++++++++++++++++++++++ | 66% ~25m 56s
|++++++++++++++++++++++++++++++++++ | 67% ~24m 48s
|++++++++++++++++++++++++++++++++++ | 68% ~23m 42s
|+++++++++++++++++++++++++++++++++++ | 69% ~22m 38s
|+++++++++++++++++++++++++++++++++++ | 70% ~21m 36s
|++++++++++++++++++++++++++++++++++++ | 71% ~20m 35s
|++++++++++++++++++++++++++++++++++++ | 72% ~19m 36s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18m 39s
|+++++++++++++++++++++++++++++++++++++ | 74% ~17m 43s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21m 50s
|++++++++++++++++++++++++++++++++++++++ | 76% ~20m 41s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~19m 34s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~18m 29s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~17m 25s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16m 23s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15m 22s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~15m 14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14m 13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13m 13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12m 15s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11m 18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10m 23s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08m 35s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07m 43s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06m 52s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06m 03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05m 14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04m 26s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03m 40s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02m 54s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02m 09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01m 25s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~42s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01h 09m 33sMC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
lab = rownames(MC.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "Mast cell markers\n(Mast cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(MC.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"),
plot = MC_Volcano_TargetsA)
The target results are given below and written to a file.
NK-cells
Comparison between the natural killer cell communities (NCAM1+), and all other communities.
NK.markers <- FindMarkers(object = scRNAseqData,
ident.1 = c("CD3+CD56+ NK I",
"CD3+CD56+ NK II"),
ident.2 = c("CD68+CASP1+IL1B+SELL MInf",
"CD68+CD1C+ DC",
"CD68+CD4+ Mono",
"CD68+IL18+TLR4+TREM2+ MRes",
"CD68+ABCA1+OLR1+TREM2+ FC",
"CD3+ TC I",
"CD3+ TC II",
"CD3+ TC III",
"CD3+ TC IV",
"CD3+ TC V",
"CD3+ TC VI",
"FOXP3+ TC",
"CD34+ EC I",
"CD34+ EC II",
"ACTA2+ SMC",
# "CD3+CD56+ NK I",
# "CD3+CD56+ NK II",
"CD68+KIT+ MC",
"CD79+ BCplasma",
"CD79A+ BCmem"))
| | 0 % ~calculating
|+ | 1 % ~36s
|++ | 2 % ~30s
|++ | 3 % ~28s
|+++ | 4 % ~27s
|+++ | 5 % ~26s
|++++ | 6 % ~26s
|++++ | 7 % ~25s
|+++++ | 8 % ~25s
|+++++ | 9 % ~24s
|++++++ | 10% ~24s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 13% ~23s
|++++++++ | 14% ~22s
|++++++++ | 15% ~22s
|+++++++++ | 16% ~22s
|+++++++++ | 18% ~21s
|++++++++++ | 19% ~21s
|++++++++++ | 20% ~24s
|+++++++++++ | 21% ~24s
|+++++++++++ | 22% ~23s
|++++++++++++ | 23% ~23s
|++++++++++++ | 24% ~23s
|+++++++++++++ | 25% ~22s
|+++++++++++++ | 26% ~22s
|++++++++++++++ | 27% ~21s
|++++++++++++++ | 28% ~21s
|+++++++++++++++ | 29% ~20s
|+++++++++++++++ | 30% ~20s
|++++++++++++++++ | 31% ~20s
|++++++++++++++++ | 32% ~19s
|+++++++++++++++++ | 33% ~19s
|++++++++++++++++++ | 34% ~19s
|++++++++++++++++++ | 35% ~18s
|+++++++++++++++++++ | 36% ~18s
|+++++++++++++++++++ | 37% ~18s
|++++++++++++++++++++ | 38% ~17s
|++++++++++++++++++++ | 39% ~17s
|+++++++++++++++++++++ | 40% ~17s
|+++++++++++++++++++++ | 41% ~16s
|++++++++++++++++++++++ | 42% ~16s
|++++++++++++++++++++++ | 43% ~16s
|+++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~15s
|++++++++++++++++++++++++ | 46% ~15s
|++++++++++++++++++++++++ | 47% ~14s
|+++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~14s
|++++++++++++++++++++++++++ | 51% ~14s
|++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~13s
|+++++++++++++++++++++++++++ | 54% ~13s
|++++++++++++++++++++++++++++ | 55% ~12s
|++++++++++++++++++++++++++++ | 56% ~12s
|+++++++++++++++++++++++++++++ | 57% ~12s
|+++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~11s
|++++++++++++++++++++++++++++++ | 60% ~11s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|+++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|++++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=26s NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
lab = rownames(NK.markers),
x = "avg_log2FC",
y = "p_val_adj",
selectLab = target_genes_qc,
axisLabSize = 12,
xlab = "average fold-change",
title = "NK markers\n(NK-cell communities vs the rest)",
titleLabSize = 14,
pCutoff = 0.05/nrow(NK.markers), # 20552 genes
FCcutoff = 1.25,
pointSize = 1.5,
labSize = 3.0,
legendLabels =c('NS','avg. fold-change','P',
'P & avg. fold-change'),
legendPosition = "right",
legendLabSize = 10,
legendIconSize = 3.0,
drawConnectors = TRUE,
widthConnectors = 0.2,
colConnectors = "#595A5C",
gridlines.major = FALSE,
gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"),
plot = NK_Volcano_TargetsA)
The target results are given below and written to a file.
Subset scRNAseq data
List of samples to be included based on informed consent (see above).
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx",
"sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time")
temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")
# str(scRNAseqDataCEA39@meta.data)Saving new dataset
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
"Plate", "Batch", "C.H", "Type", "percent.mt",
"nCount_SCT", "nFeature_SCT", "seurat_clusters")))
# fwrite(temp2,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp2)
#
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
#
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))
fwrite(temp2,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp2)
temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))Session information
Version: v1.1.1
Last update: 2023-06-14
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.1.1 Fix writing baseline table.
* v1.1.0 Update to study database.
* v1.0.2 Fixes to the start of the notebook. Update to loading of the clinical data. Fix on the gene-filtering.
* v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
* v1.0.0 Initial version.R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin22.4.0 (64-bit)
Running under: macOS 14.0
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/r/4.3.0_1/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Amsterdam
tzcode source: internal
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-3 SeuratObject_4.1.3 Seurat_4.3.0
[4] BiocManager_1.30.20 sjlabelled_1.2.0 mia_1.8.0
[7] MultiAssayExperiment_1.26.0 TreeSummarizedExperiment_2.8.0 Biostrings_2.68.1
[10] XVector_0.40.0 SingleCellExperiment_1.22.0 MASS_7.3-60
[13] magrittr_2.0.3 annotables_0.2.0 EnhancedVolcano_1.18.0
[16] ggrepel_0.9.3 AnnotationFilter_1.24.0 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[19] mygene_1.36.0 org.Hs.eg.db_3.17.0 DESeq2_1.40.1
[22] SummarizedExperiment_1.30.1 MatrixGenerics_1.12.0 matrixStats_1.0.0
[25] GenomicFeatures_1.52.0 AnnotationDbi_1.62.1 Biobase_2.60.0
[28] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0 IRanges_2.34.0
[31] S4Vectors_0.38.1 BiocGenerics_0.46.0 Hmisc_5.1-0
[34] survminer_0.4.9 survival_3.5-5 GGally_2.1.2
[37] PerformanceAnalytics_2.0.4 xts_0.13.1 zoo_1.8-12
[40] ggcorrplot_0.1.4.999 corrr_0.4.4 reshape2_1.4.4
[43] bacon_1.28.0 ellipse_0.4.5 BiocParallel_1.34.2
[46] meta_6.2-1 qqman_0.1.8 tidylog_1.0.2
[49] gridExtra_2.3 plyr_1.8.8 rmarkdown_2.22
[52] patchwork_1.1.2.9000 labelled_2.11.0 sjPlot_2.8.14
[55] UpSetR_1.4.0 ggpubr_0.6.0 forestplot_3.1.1
[58] abind_1.4-5 checkmate_2.2.0 pheatmap_1.0.12
[61] devtools_2.4.5 usethis_2.2.0 BlandAltmanLeh_0.3.1
[64] tableone_0.13.2 openxlsx_4.2.5.2 haven_2.5.2
[67] eeptools_1.2.5 DT_0.28 knitr_1.43
[70] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[73] purrr_1.0.1 tibble_3.2.1 ggplot2_3.4.2
[76] tidyverse_2.0.0 data.table_1.14.8 naniar_1.0.0
[79] tidyr_1.3.0 dplyr_1.1.2 optparse_1.7.3
[82] readr_2.1.4 pander_0.6.5 R.utils_2.12.2
[85] R.oo_1.25.0 R.methodsS3_1.8.2 worcs_0.1.10
[88] credentials_1.3.2
loaded via a namespace (and not attached):
[1] igraph_1.4.3 ica_1.0-3 plotly_4.10.2 Formula_1.2-5
[5] scater_1.28.0 zlibbioc_1.46.0 gert_1.9.2 tidyselect_1.2.0
[9] bit_4.0.5 lattice_0.21-8 rjson_0.2.21 blob_1.2.4
[13] urlchecker_1.0.1 S4Arrays_1.0.4 parallel_4.3.0 png_0.1-8
[17] tinytex_0.45 cli_3.6.1 bayestestR_0.13.1 ProtGenerics_1.32.0
[21] askpass_1.1 sjstats_0.18.2 openssl_2.0.6 goftest_1.2-3
[25] textshaping_0.3.6 BiocIO_1.10.0 BiocNeighbors_1.18.0 uwot_0.1.14
[29] curl_5.0.0 tidytree_0.4.2 mime_0.12 evaluate_0.21
[33] gsubfn_0.7 leiden_0.4.3 stringi_1.7.12 backports_1.4.1
[37] XML_3.99-0.14 httpuv_1.6.11 rappdirs_0.3.3 splines_4.3.0
[41] getopt_1.20.3 KMsurv_0.1-5 ggbeeswarm_0.7.2 sctransform_0.3.5
[45] sessioninfo_1.2.2 DBI_1.1.3 jquerylib_0.1.4 withr_2.5.0
[49] systemfonts_1.0.4 class_7.3-22 lmtest_0.9-40 rtracklayer_1.60.0
[53] htmlwidgets_1.6.2 fs_1.6.2 biomaRt_2.56.0 labeling_0.4.2
[57] gh_1.4.0 ranger_0.15.1 reticulate_1.29 decontam_1.20.0
[61] timechange_0.2.0 fansi_1.0.4 calibrate_1.7.7 vegan_2.6-4
[65] irlba_2.3.5.1 ggrastr_1.0.2 commonmark_1.9.0 ellipsis_0.3.2
[69] lazyeval_0.2.2 yaml_2.3.7 scattermore_1.1 crayon_1.5.2
[73] RcppAnnoy_0.0.20 progressr_0.13.0 later_1.3.1 ggridges_0.5.4
[77] codetools_0.2-19 base64enc_0.1-3 profvis_0.3.8 KEGGREST_1.40.0
[81] Rtsne_0.16 limma_3.56.2 estimability_1.4.1 Rsamtools_2.16.0
[85] filelock_1.0.2 rticles_0.25 sqldf_0.4-11 foreign_0.8-84
[89] pkgconfig_2.0.3 xml2_1.3.4 mathjaxr_1.6-0 GenomicAlignments_1.36.0
[93] ape_5.7-1 spatstat.sparse_3.0-1 viridisLite_0.4.2 performance_0.10.4
[97] xtable_1.8-4 car_3.1-2 httr_1.4.6 globals_0.16.2
[101] sys_3.4.2 pkgbuild_1.4.0 beeswarm_0.4.0 htmlTable_2.4.1
[105] broom_1.0.4 nlme_3.1-162 dbplyr_2.3.2 survMisc_0.5.6
[109] crosstalk_1.2.0 ggeffects_1.2.2 lme4_1.1-33 digest_0.6.31
[113] permute_0.9-7 numDeriv_2016.8-1.1 Matrix_1.5-4.1 farver_2.1.1
[117] tzdb_0.4.0 viridis_0.6.3 yulab.utils_0.0.6 DirichletMultinomial_1.42.0
[121] rpart_4.1.19 glue_1.6.2 cachem_1.0.8 BiocFileCache_2.8.0
[125] polyclip_1.10-4 generics_0.1.3 visdat_0.6.0 CompQuadForm_1.4.3
[129] mvtnorm_1.2-1 proto_1.0.0 survey_4.2-1 parallelly_1.36.0
[133] ggtext_0.1.2 pkgload_1.3.2 arm_1.13-1 ragg_1.2.5
[137] ScaledMatrix_1.8.1 carData_3.0-5 minqa_1.2.5 pbapply_1.7-0
[141] vroom_1.6.3 utf8_1.2.3 mitools_2.4 sjmisc_2.8.9
[145] ggsignif_0.6.4 shiny_1.7.4 GenomeInfoDbData_1.2.10 clisymbols_1.2.0
[149] RCurl_1.98-1.12 memoise_2.0.1 scales_1.2.1 future_1.32.0
[153] reshape_0.8.9 RANN_2.6.1 renv_0.17.3 km.ci_0.5-6
[157] spatstat.data_3.0-1 rstudioapi_0.14 cluster_2.1.4 spatstat.utils_3.0-3
[161] hms_1.1.3 fitdistrplus_1.1-11 munsell_0.5.0 cowplot_1.1.1
[165] colorspace_2.1-0 rlang_1.1.1 quadprog_1.5-8 sparseMatrixStats_1.12.0
[169] DelayedMatrixStats_1.22.0 scuttle_1.10.1 mgcv_1.8-42 xfun_0.39
[173] prereg_0.6.0 coda_0.19-4 e1071_1.7-13 TH.data_1.1-2
[177] metafor_4.2-0 modelr_0.1.11 remotes_2.4.2 emmeans_1.8.6
[181] treeio_1.24.1 ggsci_3.0.0 DECIPHER_2.28.0 bitops_1.0-7
[185] ps_1.7.5 promises_1.2.0.1 RSQLite_2.3.1 sandwich_3.0-2
[189] DelayedArray_0.26.3 proxy_0.4-27 compiler_4.3.0 prettyunits_1.1.1
[193] beachmat_2.16.0 boot_1.3-28.1 metadat_1.2-0 listenv_0.9.0
[197] Rcpp_1.0.10 BiocSingular_1.16.0 tensor_1.5 progress_1.2.2
[201] gridtext_0.1.5 insight_0.19.2 spatstat.random_3.1-5 R6_2.5.1
[205] fastmap_1.1.1 multcomp_1.4-23 rstatix_0.7.2 vipor_0.4.5
[209] ROCR_1.0-11 rsvd_1.0.5 vcd_1.4-11 nnet_7.3-19
[213] gtable_0.3.3 KernSmooth_2.23-21 miniUI_0.1.1.1 deldir_1.0-9
[217] htmltools_0.5.5 bit64_4.0.5 spatstat.explore_3.2-1 lifecycle_1.0.3
[221] zip_2.3.0 processx_3.8.1 nloptr_2.0.3 callr_3.7.3
[225] restfulr_0.0.15 sass_0.4.6 vctrs_0.6.2 spatstat.geom_3.2-1
[229] sp_1.6-1 future.apply_1.11.0 bslib_0.4.2 pillar_1.9.0
[233] locfit_1.5-9.7 jsonlite_1.8.5 markdown_1.7 chron_2.3-61 Saving environment
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))| © 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | vanderlaan.science. | |
---
title: "Mapping targets to single cells in plaques."
author: "[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: Accompanying 'AE_TEMPLATE'
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack03.packages.R")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

source("scripts/colors.R")

```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we map the `r TRAIT_OF_INTEREST` to single-cells from the plaques.

## Targets

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/targets.xlsx"), sheet = "Genes")

gene_list <- unlist(gene_list_df$Gene)
gene_list

```


# Load data

First we will load the data:

-   scRNAseq experimental data and rename the cell types.
-   Athero-Express clinical data.

Here we load the latest dataset from our Athero-Express single-cell RNA experiment.

```{r LoadData}

# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
# 
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData

```

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exhibit similar properties, *i.e.* similar defining markers (*e.g. KIT*).

We will rename the cell types to human readable names.

```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

```

```{r}
unique(scRNAseqData@active.ident)
```

```{r}
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono", 
               "CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes", 
               "CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
               "CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
               "CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
               
               # T-cells
               "CD3+ T Cells I" = "CD3+ TC I",
               "CD3+ T Cells II" = "CD3+ TC II", 
               "CD3+ T Cells III" = "CD3+ TC III", 
               "CD3+ T Cells IV" = "CD3+ TC IV", 
               "CD3+ T Cells V" = "CD3+ TC V", 
               "CD3+ T Cells VI" = "CD3+ TC VI", 
               "FOXP3+ T Cells" = "FOXP3+ TC",
               
               # Endothelial cells
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               
               # SMC
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               
               # NK Cells
               "CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
               "CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
               # Mast
               "CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
               
               "CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem", 
               "CD79+ Plasma B Cells" = "CD79+ BCplasma")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Clinical data

Loading the Athero-Express clinical data.

```{r LoadAEDB}

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20230614.",PROJECTNAME,".AEDB.CEA.RDS"))

```


```{r }

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                   "restenos", "stenose", 
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index",
                   "PCSK9_plasma", "PCSK9_plasma_rankNorm")

basetable_bin = c("Gender",  "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

## AESCRNA: baseline characteristics

### Preparation

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
```

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                                 (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                                   informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                                   informedconsent != "no, died" &
                                   informedconsent != "yes, no tissue, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no health treatment" &
                                   informedconsent != "yes, no tissue, no questionnaires" &
                                   informedconsent != "yes, no tissue, health treatment when possible" &
                                   informedconsent != "yes, no tissue" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                                   informedconsent != "no, doesn't want to" &
                                   informedconsent != "no, unable to sign" &
                                   informedconsent != "no, no reaction" &
                                   informedconsent != "no, lost" &
                                   informedconsent != "no, too old" &
                                   informedconsent != "yes, no medical info, health treatment when possible" & 
                                   informedconsent != "no (never asked for IC because there was no tissue)" &
                                   informedconsent != "no, endpoint" &
                                   informedconsent != "nooit geincludeerd" & 
                                   informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
                                   informedconsent != "yes, no tissue, no commerical business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" & 
                                   informedconsent != "yes, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no commerical business" & 
                                   informedconsent != "yes, health treatment when possible, no commercial business" & 
                                   informedconsent != "yes, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" & 
                                   informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

### Baseline

Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.32pts.after_qc.IC_commercial.BaselineTable.xlsx"), 
#            format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
#            rowNames = TRUE, colNames = TRUE, 
#            sheetName = "AESCRNA", overwrite = TRUE)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"),
           format(as.data.frame(scRNAseqDataMetaAE.all.tableOne), digits = 5, scientific = FALSE),
           rowNames = TRUE, colNames = TRUE,
           sheetName = "AESCRNA_CEA", overwrite = TRUE)

```

# AESCRNA

## Quality control

Here review the number of cells per sample, plate, and patients. And plot the
ratio's per sample and study number.

```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))

# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 1,
#         col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()

# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 2,
#         col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()



```

## Visualisations

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```

## Targets of interest:

We check whether the targets genes were sequenced using our method. In case some genes are not available in our data we could filter them here.

```{r list target genes}
target_genes <- gene_list
target_genes

```


This code is just an example to filter the list from genes that are not in the data.

- _COL3A_ ==> not found
- _COL2A_ ==> not found

```{r Visualisation: preparation}

gene_list_rm <- c("COL3A", "COL2A") 

temp = target_genes[!target_genes %in% gene_list_rm]

target_genes_qc <- c(temp)

# gene_list_qc <- gene_list
# 
# for debug
# gene_list_qc_replace <- c("MRTFA")

# target_genes_qc <- target_genes
target_genes_qc

```


### Expression in cell communities

```{r Visualisation: Targets Feature and Dot Plots, message=FALSE, warning=FALSE}
library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())

rm(p1)

# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
#             cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             combine = TRUE)
# 
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())


```

```{r Visualisation: Targets}
# VlnPlot(scRNAseqData, features = "DUSP27")

# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")), 
       dir.create(file.path(PLOT_loc, "/VlnPlot")), 
       FALSE)
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
  
  # print(vp1)
  
}

```

### Differential expression between cell communities

Here we project genes to only the broad cell communities:

-   macrophages
-   endothelial cells
-   smooth muscle cells
-   T-cells
-   B-cells
-   Mast cells
-   NK-cells
-   Mixed cells

#### Macrophages

```{r}
unique(scRNAseqData@active.ident)
```

Comparison between the macrophages cell communities (*CD14/CD68*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano MAC calculate}

MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC"), 
                          ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf", 
                                      #"CD68+CD1C+ DC", 
                                      #"CD68+CD4+ Mono",
                                      #"CD68+IL18+TLR4+TREM2+ MRes",
                                      #"CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MAC.markers)
```

```{r Visualisation: Volcano MAC, message=FALSE, warning=FALSE}
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MAC}
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MAC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Smooth muscle cells

Comparison between the smooth muscle cell communities (*ACTA2*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano SMC calculate}

SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      #"ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(SMC.markers)
```

```{r Visualisation: Volcano SMC, message=FALSE, warning=FALSE}
SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results SMC}
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results SMC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Endothelial cells

Comparison between the endothelial cell communities (*CD34*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano EC calculate}

EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", 
                                      "CD34+ EC II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(EC.markers)
```

```{r Visualisation: Volcano EC, message=FALSE, warning=FALSE}
EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results EC}
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results EC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### T-cells

Comparison between the T-cell communities (*CD3/CD4/CD8*<sup>+</sup>), and all
other communities.

```{r Visualisation: Volcano Tcell calculate}

TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      # "CD3+ TC I",
                                      # "CD3+ TC II", 
                                      # "CD3+ TC III", 
                                      # "CD3+ TC IV", 
                                      # "CD3+ TC V", 
                                      # "CD3+ TC VI", 
                                      # "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(TC.markers)
```

```{r Visualisation: Volcano Tcell, message=FALSE, warning=FALSE}
TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(TC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results TC}
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results TC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### B-cells

Comparison between the B-cell communities (*CD79A*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Bcell calculate}

BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79+ BCplasma", 
                                      "CD79A+ BCmem"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC"
                                      # "CD79+ BCplasma", 
                                      # "CD79A+ BCmem"
                                      ))

DT::datatable(BC.markers)
```

```{r Visualisation: Volcano Bcell, message=FALSE, warning=FALSE}
BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(BC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results BC}
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results BC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mast cells

Comparison between the mast cell communities (*KIT*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Mast calculate}

MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+KIT+ MC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      # "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MC.markers)
```

```{r Visualisation: Volcano Mast, message=FALSE, warning=FALSE}
MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(MC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MC}
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### NK-cells

Comparison between the natural killer cell communities (*NCAM1*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano NK calculate}

NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      # "CD3+CD56+ NK I",
                                      # "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(NK.markers)
```

```{r Visualisation: Volcano NK, message=FALSE, warning=FALSE}
NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(NK.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results NK}
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results NK: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

# Subset scRNAseq data

List of samples to be included based on informed consent (see above).

```{r}
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)

```

```{r}
scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)
```

```{r}
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
                           "Age", "Gender",
                           "TC_final", "LDL_final", "HDL_final", "TG_final",
                           "systolic", "diastoli", "GFR_MDRD", "BMI",
                           "KDOQI", "BMI_WHO",
                           "SmokerStatus", "AlcoholUse",
                           "DiabetesStatus",
                           "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
                           "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
                           "Stroke_Dx",
                           "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                           "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                           "restenos", "stenose",
                           "CAD_history", "PAOD", "Peripheral.interv",
                           "EP_composite", "EP_composite_time", "EP_major", "EP_major_time")

temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)

```

```{r}
scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")

# str(scRNAseqDataCEA39@meta.data)

```

## Saving new dataset

```{r}
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
                                                                 "Plate", "Batch", "C.H", "Type", "percent.mt",
                                                                 "nCount_SCT", "nFeature_SCT", "seurat_clusters")))

# fwrite(temp2,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp2)
# 
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))

fwrite(temp2,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp2)

temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.1.1
    Last update:  2023-06-14
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.1.1 Fix writing baseline table.
    * v1.1.0 Update to study database.
    * v1.0.2 Fixes to the start of the notebook. Update to loading of the clinical data. Fix on the gene-filtering.
    * v1.0.1 Update to main AEDB (there is an error in the Age-variable in the new version). Fewer patients in scRNAseq (32 vs 39 with the newer dataset).
    * v1.0.0 Initial version.

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))

```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [vanderlaan.science](https://vanderlaan.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+
